A genetic parallel programming based logic circuit synthesizer.

Lau, Wai Shing.Thesis submitted in: November 2006.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 85-94).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Field Programmable Gate Arrays --- p.2Chapter 1.2 --- FPGA technology mapping problem --- p.3Chapter 1.3 --- Motivations --- p.5Chapter 1.4 --- Contributions --- p.6Chapter 1.5 --- Thesis Organization --- p.9Chapter 2 --- Background Study --- p.11Chapter 2.1 --- Deterministic approach to technology mapping problem --- p.11Chapter 2.1.1 --- FlowMap --- p.12Chapter 2.1.2 --- DAOMap --- p.14Chapter 2.2 --- Stochastic approach --- p.15Chapter 2.2.1 --- Bio-Inspired Methods for Multi-Level Combinational Logic Circuit Design --- p.15Chapter 2.2.2 --- A Survey of Combinational Logic Circuit Representations in stochastic algorithms --- p.17Chapter 2.3 --- Genetic Parallel Programming --- p.20Chapter 2.3.1 --- Accelerating Phenomenon --- p.22Chapter 2.4 --- Chapter Summary --- p.23Chapter 3 --- A GPP based Logic Circuit Synthesizer --- p.24Chapter 3.1 --- Overall system architecture --- p.25Chapter 3.2 --- Multi-Logic-Unit Processor --- p.26Chapter 3.3 --- The Genotype of a MLP program --- p.28Chapter 3.4 --- The Phenotype of a MLP program --- p.31Chapter 3.5 --- The Evolution Engine --- p.33Chapter 3.5.1 --- The Dual-Phase Approach --- p.33Chapter 3.5.2 --- Genetic operators --- p.35Chapter 3.6 --- Chapter Summary --- p.38Chapter 4 --- MLP in hardware --- p.39Chapter 4.1 --- Motivation --- p.39Chapter 4.2 --- Hardware Design and Implementation --- p.40Chapter 4.3 --- Experimental Settings --- p.43Chapter 4.4 --- Experimental Results and Evaluations --- p.46Chapter 4.5 --- Chapter Summary --- p.50Chapter 5 --- Feasibility Study of Multi MLPs --- p.51Chapter 5.1 --- Motivation --- p.52Chapter 5.2 --- Overall Architecture --- p.53Chapter 5.3 --- Experimental settings --- p.55Chapter 5.4 --- Experimental results and evaluations --- p.59Chapter 5.5 --- Chapter Summary --- p.59Chapter 6 --- A Hybridized GPPLCS --- p.61Chapter 6.1 --- Motivation --- p.62Chapter 6.2 --- Overall system architecture --- p.62Chapter 6.3 --- Experimental settings --- p.64Chapter 6.4 --- Experimental results and evaluations --- p.66Chapter 6.5 --- Chapter Summary --- p.70Chapter 7 --- A Memetic GPPLCS --- p.71Chapter 7.1 --- Motivation --- p.72Chapter 7.2 --- Overall system architecture --- p.72Chapter 7.3 --- Experimental settings --- p.76Chapter 7.4 --- Experimental results and evaluations --- p.77Chapter 7.5 --- Chapter Summary --- p.80Chapter 8 --- Conclusion --- p.82Chapter 8.1 --- Future work --- p.83Bibliography --- p.8

Toward alive art

Electronics is about to change the idea of art and drastically so. We know this is going to happen - we can feel it. Much less clear to most of us are the hows, whens and whys of the change. In this paper, we will attempt to analyze the mechanisms and dynamics of the coming cultural revolution, focusing on the «artistic space» where the revolution is taking place, on the interactions between the artistic act and the space in which the act takes place and on the way in which the act modifies the space and the space the act. We briefly discuss the new category of «electronic artists». We then highlight what we see as the logical process connecting the past, the present and our uncertain future. We examine the relationship between art and previous technologies, pointing to the evolutionary, as well as the revolutionary impact of new means of expression. Against this background we propose a definition for what we call «Alive Art», going on to develop a tentative profile of the performers (the «Alivers»). In the last section, we describe two examples of Alive Artworks, pointing out the central role of what we call the "Alive Art Effect" in which we can perceive relative independence of creation from the artist and thus it may seem that unique creative role of artist is not always immediate and directly induced by his/her activity. We actually, emphasized that artist's activities may result in unpredictable processes more or less free of the artist's will

Design and Characterization of Heterochiral Strand Displacement Reactions

The nature of Watson – Crick base pairing has enabled the rational design of complex and dynamic DNA/RNA-based molecular circuits capable of detecting nucleic acids in a sequence dependent fashion in vitro. Given the ease by which DNA can be programmed to interact with living systems, DNA-based molecular circuits provide an attractive avenue for the sequence-specific detection of RNA biomarkers in live cells. However, the stability of exogenous nucleic acids in biological environments remains a major concern. In order to overcome this limitation, modifications to the ribose backbone or within the phosphodiester bond have been employed to increase the resistance of DNA probes to nucleolytic degradation. Most DNA modifications in routine use alter the thermodynamic and kinetic properties of DNA/RNA hybridization, making it difficult to design complex reaction networks that function in the cellular environment. vL-DNA, the mirror image (i.e. enantiomer) of natural vD-DNA, represents a critically underexplored modification for this application. vL-DNAs have the same physical and chemical properties as their natural counterparts, but they are essentially ‘invisible’ to the stereospecific environment of biology. However, vL-DNA cannot form contiguous WC base pairs with vD-DNA/RNA, severely limiting its use in the development of sequence-specific probes for cellular nucleic acids (summarized in Chapter 1). Chapter 2 focuses on two potential solutions to this problem, both built on the well understood rules of DNA strand displacement reactions. We report a novel toehold-mediated strand displacement reaction utilizing achiral peptide nucleic acid (PNA)/vL- DNA duplexes and demonstrate the sequence-specific recognition of vD-DNA and vD-RNA inputs. An alternative strand displacement design is also reported whereby chimeric DDNA and vL-DNA duplexes are designed such that recognition of a vD-DNA or vD-RNA input causes the concomitant melting and release of an vL-DNA output. The work presented in this section represents first of their kind heterochiral strand displacements. Following these developments, we demonstrate the stability of these heterochiral strand displacements in living cells. Direct comparisons are made to vD-DNA components, as well as components containing the common 2′-O-methyl modification. This section underscores the potential stability of vL-DNA circuits, as well as the ‘plug-and-play’ utility of adapting vD-DNA circuit designs to vL-DNA. Finally, we design a model system in chapter 4 to thoroughly characterize heterochiral strand displacement. We show that strand displacements using PNA are generally slower than their all-DNA counterparts, and that in every case strand displacement occurs slower when the input and output chirality are not matched. Interestingly, this heterochiral barrier to strand displacement enhances the mismatch discrimination of heterochiral strand displacement systems. Overall, this work identifies dynamic molecular systems capable of sequence-specifically recognizing a vD-DNA input and generating an vL-DNA output. These heterochiral strand displacements are fast, biostable, and lay the foundation for the design of computational DNA systems capable of identifying endogenous nucleic acids inside live cells

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Engineering DNA Computation with Unnatural Nucleotides and Protein Function with Unnatural Amino Acids

Synthetic engineering methods, such as DNA computation and unnatural amino acid mutagenesis, have provided a route to improve the control of DNA and proteins. DNA computation encompasses a broad field that attempts to build computational devices from DNA structures. Logic gates are a fundamental component of any larger computational network, and have been constructed from purely DNA frameworks. Operation is determined by strict rules, which allow for the predictable creation of complex circuits. Described herein are methods to alter DNA logic gates with photochemical caging groups, interface logic gates with protein output, and optically control DNA amplification cycles. These methods have enabled precise temporal and spatial control, as well as merged the interface between DNA circuits and biological systems. Unnatural amino acid mutagenesis enables the site specific alteration of protein residues, through the insertion of a non-canonical amino acid. Incorporation of these unnatural residues has greatly expanded the function of proteins, with the introduction of new chemical functionalities. These chemical handles have enabled applications such as the study of abasic bypass in DNA polymerases and protein-RNA crosslinking

Microarray image processing : a novel neural network framework

Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.EThOS - Electronic Theses Online ServiceAleppo University, SyriaGBUnited Kingdo

Dynamic DNA Nanotechnology for Probing Single Nucleotide Variants and DNA Modifications

In the last decades, various DNA hybridization probes have been developed that attempt to conquer the challenge of single-nucleotide-variants (SNVs) detection. Even though a powerful toolbox including the toehold-exchange reaction, the dynamic ‘sink’ design, and the polymerase chain reaction (PCR) has been built, it still faces practical problems. For example, the natural DNA is usually in double-stranded form whereas most hybridization probes aim for single-stranded targets; the concentration of extracted DNA samples is totally unknown thus may lay outside the optimal design of probes/primers. To achieve ultra-high sensitivity and specificity, expensive and sophisticated machines such as digital droplet PCR and next-generation-sequencing may be inapplicable in rural areas. Therefore, the quantitative PCR method is still the gold standard for clinical tests. Thus motivated, my PhD career was mainly focused on the fundamental understanding of the challenges in SNVs discrimination and developing robust, versatile, and user-friendly probes/strategies. In this thesis, Chapter 1 provides a general introduction of dynamic DNA nanotechnology and its representative applications in discriminating SNVs. Chapter 2 to 4 describe three completed projects that aim to understand the thermodynamic and kinetic properties of strand displacement reactions and to circumvent the challenges of discriminating SNVs through finely tuned probes/assays

A hardware-software codesign framework for cellular computing

Until recently, the ever-increasing demand of computing power has been met on one hand by increasing the operating frequency of processors and on the other hand by designing architectures capable of exploiting parallelism at the instruction level through hardware mechanisms such as super-scalar execution. However, both these approaches seem to have reached a plateau, mainly due to issues related to design complexity and cost-effectiveness. To face the stabilization of performance of single-threaded processors, the current trend in processor design seems to favor a switch to coarser-grain parallelization, typically at the thread level. In other words, high computational power is achieved not only by a single, very fast and very complex processor, but through the parallel operation of several processors, each executing a different thread. Extrapolating this trend to take into account the vast amount of on-chip hardware resources that will be available in the next few decades (either through further shrinkage of silicon fabrication processes or by the introduction of molecular-scale devices), together with the predicted features of such devices (e.g., the impossibility of global synchronization or higher failure rates), it seems reasonable to foretell that current design techniques will not be able to cope with the requirements of next-generation electronic devices and that novel design tools and programming methods will have to be devised. A tempting source of inspiration to solve the problems implied by a massively parallel organization and inherently error-prone substrates is biology. In fact, living beings possess characteristics, such as robustness to damage and self-organization, which were shown in previous research as interesting to be implemented in hardware. For instance, it was possible to realize relatively simple systems, such as a self-repairing watch. Overall, these bio-inspired approaches seem very promising but their interest for a wider audience is problematic because their heavily hardware-oriented designs lack some of the flexibility achievable with a general purpose processor. In the context of this thesis, we will introduce a processor-grade processing element at the heart of a bio-inspired hardware system. This processor, based on a single-instruction, features some key properties that allow it to maintain the versatility required by the implementation of bio-inspired mechanisms and to realize general computation. We will also demonstrate that the flexibility of such a processor enables it to be evolved so it can be tailored to different types of applications. In the second half of this thesis, we will analyze how the implementation of a large number of these processors can be used on a hardware platform to explore various bio-inspired mechanisms. Based on an extensible platform of many FPGAs, configured as a networked structure of processors, the hardware part of this computing framework is backed by an open library of software components that provides primitives for efficient inter-processor communication and distributed computation. We will show that this dual software–hardware approach allows a very quick exploration of different ways to solve computational problems using bio-inspired techniques. In addition, we also show that the flexibility of our approach allows it to exploit replication as a solution to issues that concern standard embedded applications

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest